18258798. METHOD FOR PARTITIONING TIME SERIES simplified abstract (CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE)
Contents
METHOD FOR PARTITIONING TIME SERIES
Organization Name
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE
Inventor(s)
Badr Mansouri of MOISSY-CRAMAYEL (FR)
Alexandre Eid of MOISSY-CRAMAYEL (FR)
Guy Clerc of MOISSY-CRAMAYEL (FR)
METHOD FOR PARTITIONING TIME SERIES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18258798 titled 'METHOD FOR PARTITIONING TIME SERIES
Simplified Explanation:
The patent application describes a method for partitioning data by creating a primary image based on distance values between elements of time series data, segmenting the image using a learning algorithm, defining boundary signals, and merging them to create a global boundary signal for class definition.
Key Features and Innovation:
- Acquiring an observation matrix with time series data
- Calculating distance matrices for each time series
- Generating a primary image based on distance values
- Segmenting the image using a learning algorithm
- Defining boundary signals from the segmented image
- Merging boundary signals to create a global boundary signal
- Defining classes based on the global boundary signal
Potential Applications: This technology can be applied in various fields such as image processing, data analysis, pattern recognition, and machine learning.
Problems Solved: This method addresses the need for efficient data partitioning and boundary signal definition in complex datasets.
Benefits:
- Improved segmentation of data
- Enhanced boundary signal representation
- Simplified class definition process
Commercial Applications: Potential commercial uses include data mining software, image recognition systems, and predictive analytics tools. This technology can have significant market implications in industries such as healthcare, finance, and marketing.
Prior Art: Readers can explore prior research on data partitioning methods, image segmentation algorithms, and boundary signal processing techniques to understand the background of this innovation.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms, data clustering techniques, and image processing methods relevant to this technology.
Questions about Data Partitioning: 1. How does this method improve upon existing data partitioning techniques? 2. What are the key factors to consider when defining boundary signals in complex datasets?
Original Abstract Submitted
A partitioning method includes the steps of acquiring an observation matrix including time series (xx, . . . , x); for each time series calculating a distance matrix comprising distance values between the elements of the time series, then generating the primary image on the basis of the distance matrix; implementing a learning algorithm for segmenting the primary image so as to obtain a segmented image; defining, on the basis of the segmented image, a primary boundary signal representative of the boundaries; and merging the primary boundary signals in order to obtain a global boundary signal, and defining classes on the basis of the global boundary signal.
